Local Gaussian process extrapolation for BART models with applications to causal inference
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
Original language | English |
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Pages (from-to) | 724-735 |
Journal / Publication | Journal of Computational and Graphical Statistics |
Volume | 33 |
Issue number | 2 |
Online published | 26 Jul 2023 |
Publication status | Published - 2024 |
Link(s)
Abstract
Bayesian additive regression trees (BART) is a semi-parametric regression model offering state-of-the-art performance on out-of-sample prediction. Despite this success, standard implementations of BART typically suffer from inaccurate prediction and overly narrow prediction intervals at points outside the range of the training data. This paper proposes a novel extrapolation strategy that grafts Gaussian processes to the leaf nodes in BART for predicting points outside the range of the observed data. The new method is compared to standard BART implementations and recent frequentist resampling-based methods for predictive inference. We apply the new approach to a challenging problem from causal inference, wherein for some regions of predictor space, only treated or untreated units are observed (but not both). In simulation studies, the new approach boasts superior performance compared to popular alternatives, such as Jackknife+. © 2023 American Statistical Association and Institute of Mathematical Statistics
Research Area(s)
- Tree, Extrapolation, Gaussian process, Predictive interval, XBART, XBCF
Bibliographic Note
Full text of this publication does not contain sufficient affiliation information. With consent from the author(s) concerned, the Research Unit(s) information for this record is based on the existing academic department affiliation of the author(s).
Citation Format(s)
Local Gaussian process extrapolation for BART models with applications to causal inference. / Wang, Meijia; He, Jingyu; Hahn, P. Richard.
In: Journal of Computational and Graphical Statistics, Vol. 33, No. 2, 2024, p. 724-735.
In: Journal of Computational and Graphical Statistics, Vol. 33, No. 2, 2024, p. 724-735.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review